import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_SCREAMING_SNAKE_CASE 		=      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 if "token" in model_name_or_path:
  return "rag_token"
 if "sequence" in model_name_or_path:
  return "rag_sequence"
 if "bart" in model_name_or_path:
  return "bart"
 return None





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 return max(metric_fn(UpperCamelCase_							,					UpperCamelCase_       ) for gt in ground_truths       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      [line.strip() for line in open(UpperCamelCase_							,					"""r"""       ).readlines()]
 UpperCamelCase			  =      []

 if args.gold_data_mode == "qa":
  UpperCamelCase			  =      pd.read_csv(UpperCamelCase_							,					sep="""\t"""							,					header=UpperCamelCase_       )
  for answer_list in data[1]:
   UpperCamelCase			  =      ast.literal_eval(UpperCamelCase_       )
   answers.append(UpperCamelCase_       )
 else:
  UpperCamelCase			  =      [line.strip() for line in open(UpperCamelCase_							,					"""r"""       ).readlines()]
  UpperCamelCase			  =      [[reference] for reference in references]

 UpperCamelCase			  =      UpperCamelCase			  =      UpperCamelCase			  =      0
 for prediction, ground_truths in zip(UpperCamelCase_							,					UpperCamelCase_       ):
  total += 1
  em += metric_max_over_ground_truths(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  fa += metric_max_over_ground_truths(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 UpperCamelCase			  =      1_0_0.0 * em / total
 UpperCamelCase			  =      1_0_0.0 * fa / total

 logger.info(f"""F1: {fa:.2f}"""       )
 logger.info(f"""EM: {em:.2f}"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      args.k
 UpperCamelCase			  =      [line.strip() for line in open(UpperCamelCase_							,					"""r"""       ).readlines()]
 UpperCamelCase			  =      [line.strip() for line in open(UpperCamelCase_							,					"""r"""       ).readlines()]

 UpperCamelCase			  =      UpperCamelCase			  =      0
 for hypo, reference in zip(UpperCamelCase_							,					UpperCamelCase_       ):
  UpperCamelCase			  =      set(hypo.split("""\t"""       )[:k]       )
  UpperCamelCase			  =      set(reference.split("""\t"""       )       )
  total += 1
  em += len(hypo_provenance & ref_provenance       ) / k

 UpperCamelCase			  =      1_0_0.0 * em / total
 logger.info(f"""Precision@{k}: {em: .2f}"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 def strip_title(UpperCamelCase_       ):
  if title.startswith("""\""""       ):
   UpperCamelCase			  =      title[1:]
  if title.endswith("""\""""       ):
   UpperCamelCase			  =      title[:-1]
  return title

 UpperCamelCase			  =      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
     UpperCamelCase_							,					return_tensors="""pt"""							,					padding=UpperCamelCase_							,					truncation=UpperCamelCase_							,					)["""input_ids"""].to(args.device       )

 UpperCamelCase			  =      rag_model.rag.question_encoder(UpperCamelCase_       )
 UpperCamelCase			  =      question_enc_outputs[0]

 UpperCamelCase			  =      rag_model.retriever(
     UpperCamelCase_							,					question_enc_pool_output.cpu().detach().to(torch.floataa       ).numpy()							,					prefix=rag_model.rag.generator.config.prefix							,					n_docs=rag_model.config.n_docs							,					return_tensors="""pt"""							,					)
 UpperCamelCase			  =      rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
 UpperCamelCase			  =      []
 for docs in all_docs:
  UpperCamelCase			  =      [strip_title(UpperCamelCase_       ) for title in docs["""title"""]]
  provenance_strings.append("""\t""".join(UpperCamelCase_       )       )
 return provenance_strings





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 with torch.no_grad():
  UpperCamelCase			  =      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      UpperCamelCase_							,					return_tensors="""pt"""							,					padding=UpperCamelCase_							,					truncation=UpperCamelCase_       )

  UpperCamelCase			  =      inputs_dict.input_ids.to(args.device       )
  UpperCamelCase			  =      inputs_dict.attention_mask.to(args.device       )
  UpperCamelCase			  =      rag_model.generate(  # rag_model overwrites generate
      UpperCamelCase_							,					attention_mask=UpperCamelCase_							,					num_beams=args.num_beams							,					min_length=args.min_length							,					max_length=args.max_length							,					early_stopping=UpperCamelCase_							,					num_return_sequences=1							,					bad_words_ids=[[0, 0]]							,					)
  UpperCamelCase			  =      rag_model.retriever.generator_tokenizer.batch_decode(UpperCamelCase_							,					skip_special_tokens=UpperCamelCase_       )

  if args.print_predictions:
   for q, a in zip(UpperCamelCase_							,					UpperCamelCase_       ):
    logger.info("""Q: {} - A: {}""".format(UpperCamelCase_							,					UpperCamelCase_       )       )

  return answers





def 			lowercase(	)		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      argparse.ArgumentParser()
 parser.add_argument(
     """--model_type"""							,					choices=["""rag_sequence""", """rag_token""", """bart"""]							,					type=UpperCamelCase_							,					help=(
         """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
         """ model_name_or_path"""
     )							,					)
 parser.add_argument(
     """--index_name"""							,					default=UpperCamelCase_							,					choices=["""exact""", """compressed""", """legacy"""]							,					type=UpperCamelCase_							,					help="""RAG model retriever type"""							,					)
 parser.add_argument(
     """--index_path"""							,					default=UpperCamelCase_							,					type=UpperCamelCase_							,					help="""Path to the retrieval index"""							,					)
 parser.add_argument("""--n_docs"""							,					default=5							,					type=UpperCamelCase_							,					help="""Number of retrieved docs"""       )
 parser.add_argument(
     """--model_name_or_path"""							,					default=UpperCamelCase_							,					type=UpperCamelCase_							,					required=UpperCamelCase_							,					help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""							,					)
 parser.add_argument(
     """--eval_mode"""							,					choices=["""e2e""", """retrieval"""]							,					default="""e2e"""							,					type=UpperCamelCase_							,					help=(
         """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
         """ precision@k."""
     )							,					)
 parser.add_argument("""--k"""							,					default=1							,					type=UpperCamelCase_							,					help="""k for the precision@k calculation"""       )
 parser.add_argument(
     """--evaluation_set"""							,					default=UpperCamelCase_							,					type=UpperCamelCase_							,					required=UpperCamelCase_							,					help="""Path to a file containing evaluation samples"""							,					)
 parser.add_argument(
     """--gold_data_path"""							,					default=UpperCamelCase_							,					type=UpperCamelCase_							,					required=UpperCamelCase_							,					help="""Path to a tab-separated file with gold samples"""							,					)
 parser.add_argument(
     """--gold_data_mode"""							,					default="""qa"""							,					type=UpperCamelCase_							,					choices=["""qa""", """ans"""]							,					help=(
         """Format of the gold data file"""
         """qa - a single line in the following format: question [tab] answer_list"""
         """ans - a single line of the gold file contains the expected answer string"""
     )							,					)
 parser.add_argument(
     """--predictions_path"""							,					type=UpperCamelCase_							,					default="""predictions.txt"""							,					help="""Name of the predictions file, to be stored in the checkpoints directory"""							,					)
 parser.add_argument(
     """--eval_all_checkpoints"""							,					action="""store_true"""							,					help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""							,					)
 parser.add_argument(
     """--eval_batch_size"""							,					default=8							,					type=UpperCamelCase_							,					help="""Batch size per GPU/CPU for evaluation."""							,					)
 parser.add_argument(
     """--recalculate"""							,					help="""Recalculate predictions even if the prediction file exists"""							,					action="""store_true"""							,					)
 parser.add_argument(
     """--num_beams"""							,					default=4							,					type=UpperCamelCase_							,					help="""Number of beams to be used when generating answers"""							,					)
 parser.add_argument("""--min_length"""							,					default=1							,					type=UpperCamelCase_							,					help="""Min length of the generated answers"""       )
 parser.add_argument("""--max_length"""							,					default=50							,					type=UpperCamelCase_							,					help="""Max length of the generated answers"""       )

 parser.add_argument(
     """--print_predictions"""							,					action="""store_true"""							,					help="""If True, prints predictions while evaluating."""							,					)
 parser.add_argument(
     """--print_docs"""							,					action="""store_true"""							,					help="""If True, prints docs retried while generating."""							,					)
 UpperCamelCase			  =      parser.parse_args()
 UpperCamelCase			  =      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""       )
 return args





def 			lowercase(	UpperCamelCase_       )		->	Optional[int]:







 '''simple docstring'''
 UpperCamelCase			  =      {}
 if args.model_type is None:
  UpperCamelCase			  =      infer_model_type(args.model_name_or_path       )
  assert args.model_type is not None
 if args.model_type.startswith("""rag"""       ):
  UpperCamelCase			  =      RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
  UpperCamelCase			  =      args.n_docs
  if args.index_name is not None:
   UpperCamelCase			  =      args.index_name
  if args.index_path is not None:
   UpperCamelCase			  =      args.index_path
 else:
  UpperCamelCase			  =      BartForConditionalGeneration

 UpperCamelCase			  =      (
     [f.path for f in os.scandir(args.model_name_or_path       ) if f.is_dir()]
     if args.eval_all_checkpoints
     else [args.model_name_or_path]
 )

 logger.info("""Evaluate the following checkpoints: %s"""							,					UpperCamelCase_       )

 UpperCamelCase			  =      get_scores if args.eval_mode == """e2e""" else get_precision_at_k
 UpperCamelCase			  =      evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

 for checkpoint in checkpoints:
  if os.path.exists(args.predictions_path       ) and (not args.recalculate):
   logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path       )       )
   score_fn(UpperCamelCase_							,					args.predictions_path							,					args.gold_data_path       )
   continue

  logger.info("""***** Running evaluation for {} *****""".format(UpperCamelCase_       )       )
  logger.info("""  Batch size = %d"""							,					args.eval_batch_size       )
  logger.info("""  Predictions will be stored under {}""".format(args.predictions_path       )       )

  if args.model_type.startswith("""rag"""       ):
   UpperCamelCase			  =      RagRetriever.from_pretrained(UpperCamelCase_							,					**UpperCamelCase_       )
   UpperCamelCase			  =      model_class.from_pretrained(UpperCamelCase_							,					retriever=UpperCamelCase_							,					**UpperCamelCase_       )
   model.retriever.init_retrieval()
  else:
   UpperCamelCase			  =      model_class.from_pretrained(UpperCamelCase_							,					**UpperCamelCase_       )
  model.to(args.device       )

  with open(args.evaluation_set							,					"""r"""       ) as eval_file, open(args.predictions_path							,					"""w"""       ) as preds_file:
   UpperCamelCase			  =      []
   for line in tqdm(UpperCamelCase_       ):
    questions.append(line.strip()       )
    if len(UpperCamelCase_       ) == args.eval_batch_size:
     UpperCamelCase			  =      evaluate_batch_fn(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
     preds_file.write("""\n""".join(UpperCamelCase_       ) + """\n"""       )
     preds_file.flush()
     UpperCamelCase			  =      []
   if len(UpperCamelCase_       ) > 0:
    UpperCamelCase			  =      evaluate_batch_fn(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
    preds_file.write("""\n""".join(UpperCamelCase_       )       )
    preds_file.flush()

   score_fn(UpperCamelCase_							,					args.predictions_path							,					args.gold_data_path       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      get_args()
     main(args)

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
